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Table 2 Training times (in hours) for each clinical NLP task and modeling paradigm. Values in parentheses indicate how many times longer the “Continue pre-training, fine-tune & predict” paradigm takes compared to the “Fine-tune & predict” paradigm. All training times were measured using a single NVIDIA RTX 4090 GPU. It is important to note that the reported times for the “Continue pre-training, fine-tune & predict” paradigm include the continuation of pre-training, which needs to be performed only once and can then be reused for multiple tasks

From: NLP modeling recommendations for restricted data availability in clinical settings

Model & paradigm

Prioritization

Specialty

NER

xlm-roberta

   

    Fine-tune & predict

3.81

9.39

0.09

    Cont. pre-train., fine-tune & pred.

13.56 (3.6x)

19.14 (2.0x)

9.84 (109.3x)

roberta-bne

   

    Fine-tune & predict

4.13

9.46

0.11

    Cont. pre-train., fine-tune & pred.

13.88 (3.4x)

19.21 (2.0x)

9.86 (89.6x)

roberta-biomedical-clinical

   

    Fine-tune & predict

4.02

9.68

0.10

    Cont. pre-train., fine-tune & pred.

13.77 (3.4x)

19.43 (2.0x)

9.85 (98.5x)